The research applies machine recognition methods to the problem of differentiating between different classes of breast tumor using pulsed echo ultrasound information. We use a commercial water-coupled breast scanner interfaced to a PDP 11-34 computer. We are searching for characteristic features in the echoes from the tumor region that will permit the identification of the tumor type with a high degree of confidence. The features being examined include a) simple statistical quantities derived from the gray scale histogram such as bin ratios and the various distribution moments; b) texture measurement parameters such as are derived from a min-max analysis or Markov modelling of the signal; c) parameters that quantitatively describe observable features in the image such as retro-tumor enhancement or shadowing and d) features derived from a power spectral analysis of signal.